{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Disabled automatic differentiation\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "from typing import List, Optional, Union\n",
    "import sys\n",
    "sys.path.append('..')\n",
    "import einops\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "import torch\n",
    "from fancy_einsum import einsum\n",
    "from IPython.display import HTML, IFrame\n",
    "from jaxtyping import Float\n",
    "import sys\n",
    "import os\n",
    "import json\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import transformers\n",
    "import seaborn as sns\n",
    "import transformer_lens.utils as utils\n",
    "from transformer_lens import ActivationCache, HookedTransformer, SVDInterpreter\n",
    "torch.set_grad_enabled(False)\n",
    "print(\"Disabled automatic differentiation\")\n",
    "plt.rcParams['font.sans-serif']=['Times New Roman']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def decode_tokens(tokenizer, token_array):\n",
    "    if hasattr(token_array, \"shape\") and len(token_array.shape) > 1:\n",
    "        return [decode_tokens(tokenizer, row) for row in token_array]\n",
    "    return [tokenizer.decode([t]) for t in token_array]\n",
    "def find_sublist_index(main_list, sub_list):\n",
    "    main_list = main_list.tolist()\n",
    "    sub_list = sub_list.tolist()\n",
    "    index = main_list.index(sub_list[0])\n",
    "    return (index, index+len(sub_list))\n",
    "def check_subrange(tokens,sublist,range):\n",
    "    subtokens = tokens[range[0]:range[1]]\n",
    "    assert len(sublist) == len(subtokens)\n",
    "    for x,y in zip(sublist,subtokens):\n",
    "        if x==y:\n",
    "            continue\n",
    "        else:\n",
    "            return False\n",
    "    return True\n",
    "def find_token_range(tokenizer, token_array, substring):\n",
    "    toks = decode_tokens(tokenizer, token_array)\n",
    "    whole_string = \"\".join(toks)\n",
    "    char_loc = whole_string.index(substring)\n",
    "    loc = 0\n",
    "    tok_start, tok_end = None, None\n",
    "    for i, t in enumerate(toks):\n",
    "        loc += len(t)\n",
    "        if tok_start is None and loc > char_loc:\n",
    "            tok_start = i\n",
    "        if tok_end is None and loc >= char_loc + len(substring):\n",
    "            tok_end = i + 1\n",
    "            break\n",
    "    return (tok_start, tok_end)\n",
    "class ComponentAnalyzer:\n",
    "    def __init__(self, model, prompt,answer,subject) -> None:\n",
    "        # List of prompts\n",
    "        self.prompts = [prompt]\n",
    "        # print(prompt)\n",
    "        tokens = model.to_tokens(self.prompts, prepend_bos=True)\n",
    "        str_tokens = model.to_str_tokens(self.prompts, prepend_bos=True)\n",
    "        self.tokens = tokens\n",
    "        self.str_tokens = str_tokens\n",
    "        if 'llama' in model.cfg.model_name:\n",
    "            subject_range = find_sublist_index(tokens[0], model.to_tokens(subject, prepend_bos=False)[0])\n",
    "            assert check_subrange(tokens[0], model.to_tokens(subject, prepend_bos=False)[0],subject_range)\n",
    "        else:\n",
    "            subject_range = find_token_range(model.tokenizer, tokens[0],subject)\n",
    "        # List of answers, in the format (correct, incorrect)\n",
    "        self.answer_token = model.to_tokens(answer, prepend_bos=False)[0][0]\n",
    "        self.subject_range = subject_range\n",
    "        # self.subject_pos = subject_range[-1]-1\n",
    "        self.subject_last_token = tokens[0][subject_range[-1]-1]\n",
    "        self.logits,self.cache = model.run_with_cache(tokens)\n",
    "        self.accum_resid, self.labels = self.cache.accumulated_resid(incl_mid=False,mlp_input=True,return_labels=True, apply_ln=True)\n",
    "        self.heads_out = self.get_heads_out(model)\n",
    "        self.mlp_out = self.get_mlp_out(model)\n",
    "    def get_min_rank_at_subject(self, W_U, token_num):\n",
    "        last_token_accum = self.accum_resid[:, 0, self.subject_range[0]:self.subject_range[1], :] \n",
    "        layers_unembedded = einsum(\n",
    "                \"layer ... d_model, d_model d_vocab -> layer ... d_vocab\",\n",
    "                last_token_accum,\n",
    "                W_U,\n",
    "            )\n",
    "        sorted_indices = torch.argsort(layers_unembedded, dim=2, descending=True)\n",
    "        rank_answer = (sorted_indices == token_num).nonzero(as_tuple=True)[2].view(layers_unembedded.size(0),-1)\n",
    "        return rank_answer.min(dim=-1)[0]\n",
    "    \n",
    "    def get_token_rank(self, W_U, token_num, pos=-1):\n",
    "        last_token_accum = self.accum_resid[:, 0, pos, :] \n",
    "        layers_unembedded = einsum(\n",
    "                \"layer d_model, d_model d_vocab -> layer d_vocab\",\n",
    "                last_token_accum,\n",
    "                W_U,\n",
    "            )\n",
    "        sorted_indices = torch.argsort(layers_unembedded, dim=1, descending=True)\n",
    "        rank_answer = (sorted_indices == token_num).nonzero(as_tuple=True)[1]\n",
    "        return rank_answer\n",
    "    def get_token_logits(self,model,tokens,pos=-1):\n",
    "        answer_residual_directions = model.tokens_to_residual_directions(tokens)\n",
    "        # print(\"Answer residual directions shape:\", answer_residual_directions.shape)\n",
    "        logit_diff_directions = (\n",
    "            answer_residual_directions[:, ]\n",
    "        )\n",
    "        if len(logit_diff_directions.shape) ==1:\n",
    "            logit_diff_directions = logit_diff_directions.unsqueeze(0)\n",
    "        scaled_residual_stack = self.cache.apply_ln_to_stack(\n",
    "                self.accum_resid, layer=-1, pos_slice=-1\n",
    "            )\n",
    "        # print(scaled_residual_stack.shape)\n",
    "        return einsum(\n",
    "                \"... batch d_model, batch d_model -> ...\",\n",
    "                scaled_residual_stack,\n",
    "                logit_diff_directions,\n",
    "            )\n",
    "    def get_token_probability(self,model,tokens,pos=-1):\n",
    "        last_token_accum = self.accum_resid[:, 0, pos, :] \n",
    "        layers_unembedded = einsum(\n",
    "                \"layer d_model, d_model d_vocab -> layer d_vocab\",\n",
    "                last_token_accum,\n",
    "                model.W_U,\n",
    "            )\n",
    "        probs = layers_unembedded.softmax(dim=-1)\n",
    "        # print(scaled_residual_stack.shape)\n",
    "        return probs[:,tokens]\n",
    "    def get_heads_out(self, model,pos_slice=-1):\n",
    "        per_head_residual, labels = self.cache.stack_head_results(\n",
    "            layer=-1, pos_slice=pos_slice, return_labels=True, apply_ln=True\n",
    "        )\n",
    "        heads_out = {}\n",
    "        for index, label in enumerate(labels):\n",
    "            # Set the label\n",
    "            layer = index // model.cfg.n_heads\n",
    "            head_index = index % model.cfg.n_heads\n",
    "            assert f\"L{layer}H{head_index}\"==label\n",
    "            heads_out[label] = per_head_residual[index,:]\n",
    "        return heads_out\n",
    "    def get_mlp_out(self,model,pos_slice=-1):\n",
    "        per_layer_residual, labels = self.cache.decompose_resid(\n",
    "            mode='mlp',layer=-1, pos_slice=-1, return_labels=True,apply_ln= True\n",
    "        )\n",
    "        mlp_out= {}\n",
    "        for x,y in zip(per_layer_residual, labels):\n",
    "            mlp_out[y] = x\n",
    "        return mlp_out\n",
    "    def get_component_logits(self, output, model):\n",
    "        # print(heads_out[head_name].shape)\n",
    "        layers_unembedded = einsum(\n",
    "                \" ... d_model, d_model d_vocab -> ... d_vocab\",\n",
    "                output,\n",
    "                model.W_U,\n",
    "            )\n",
    "        sorted_indices = torch.argsort(layers_unembedded, dim=1, descending=True)\n",
    "        temp_logits = layers_unembedded[0]\n",
    "        tmp_sorted_indices = sorted_indices[0]\n",
    "        for i in range(10):\n",
    "            print(\n",
    "                f\"Top {i}th token. Logit: {temp_logits[tmp_sorted_indices[i]].item():5.2f} Token: |{model.to_string(tmp_sorted_indices[i])}|\"\n",
    "            )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "def draw_output_pattern_with_text(component, model, top_k=10):\n",
    "    layers_unembedded = einsum(\n",
    "            \" ... d_model, d_model d_vocab -> ... d_vocab\",\n",
    "            component,\n",
    "            model.W_U,\n",
    "        )\n",
    "    sorted_indices  = torch.argsort(layers_unembedded, dim=1, descending=True)\n",
    "    temp_logits = layers_unembedded[0]\n",
    "    tmp_sorted_indices = sorted_indices[0]\n",
    "    top_logits = []\n",
    "    top_tokens = []\n",
    "    for i in range(top_k):\n",
    "        top_logits.append(temp_logits[tmp_sorted_indices[i]].item())\n",
    "        top_tokens.append(model.to_string(tmp_sorted_indices[i]))\n",
    "    top_logits = np.expand_dims(np.array(top_logits),axis=-1)\n",
    "    top_tokens = np.expand_dims(np.array(top_tokens),axis=-1)\n",
    "    # 设置图形大小\n",
    "    plt.figure(figsize=(1.5, 6),dpi=300)\n",
    "    # 使用seaborn绘制热力图\n",
    "    sns.heatmap(top_logits, annot=top_tokens, fmt='', cmap='Blues', cbar=True,xticklabels=False, yticklabels=False)\n",
    "    # 添加标签和标题\n",
    "    # plt.xlabel('Tokens')\n",
    "    # plt.ylabel('Logits')\n",
    "    # plt.title('Top Tokens Logits Heatmap')\n",
    "    plt.show()\n",
    "def draw_attention_pattern(Component,model,layer,head_index):\n",
    "    fig = px.imshow(\n",
    "        Component.cache[\"attn\", layer][0, head_index][1:, 1:].cpu().numpy(),\n",
    "        title=f\"{layer}.{head_index} Attention\",\n",
    "        color_continuous_midpoint=0,\n",
    "        color_continuous_scale=\"RdBu\",\n",
    "        labels={\"y\": \"Queries\", \"x\": \"Keys\"},\n",
    "        height=500,\n",
    "    )\n",
    "\n",
    "    fig.update_layout(\n",
    "        xaxis={\n",
    "            \"side\": \"top\",\n",
    "            \"ticktext\": Component.str_tokens[0][1:],\n",
    "            \"tickvals\": list(range(len(Component.tokens[0])-1)),\n",
    "            \"tickfont\": dict(size=15),\n",
    "        },\n",
    "        yaxis={\n",
    "            \"ticktext\": Component.str_tokens[0][1:],\n",
    "            \"tickvals\": list(range(len(Component.tokens[0])-1)),\n",
    "            \"tickfont\": dict(size=15),\n",
    "        },\n",
    "    )\n",
    "    # fig.write_image(f\"{layer}.{head_index}_Attention.pdf\")\n",
    "    fig.show()\n",
    "def draw_rank_logits(gpt2_medium, China):\n",
    "    x=np.arange(gpt2_medium.cfg.n_layers+1)\n",
    "    fig=plt.figure(figsize=(8,4),dpi=100)\n",
    "    y1= China.get_token_rank(gpt2_medium.W_U,China.answer_token,pos=-1).cpu(),\n",
    "    y1=utils.to_numpy(y1)[0]\n",
    "    y2= China.get_token_probability(gpt2_medium,China.answer_token,pos=-1).squeeze(-1).cpu()\n",
    "    y4= China.get_token_probability(gpt2_medium,China.subject_last_token,pos=-1).squeeze(-1).cpu()\n",
    "    y2=utils.to_numpy(y2)\n",
    "    # y3= China.get_token_rank(gpt2_medium.W_U,gpt2_medium.to_single_token(' Malaysian'),pos=-1).cpu()\n",
    "    y3 = China.get_min_rank_at_subject(gpt2_medium.W_U,China.answer_token).cpu()\n",
    "    y3=utils.to_numpy(y3)\n",
    "    #ax1显示y1  ,ax2显示y2 \n",
    "    ax1=fig.subplots()\n",
    "    ax1.plot(x, y1, 'g-', label='Target Entity at Last Position')\n",
    "    ax1.plot(x, y3, 'r-', label='Target Entity at Subject Position')\n",
    "    # ax1.grid(True)\n",
    "\n",
    "    ax2 = ax1.twinx()\n",
    "    ax2.set_ylim([0, 1])\n",
    "    ax2.plot(x, y2, 'b--', label='Prob of Object Entity')\n",
    "    ax2.plot(x, y4, 'g--', label='Prob of Subject Entity')\n",
    "\n",
    "    ax1.set_xticks(np.arange(0, 24))\n",
    "    ax1.set_xlabel('layer')\n",
    "    ax1.set_yscale('log')\n",
    "    ax1.set_ylabel('rank')\n",
    "    ax2.set_ylabel('logits')\n",
    "\n",
    "    # 添加图例\n",
    "    ax1.legend(loc='upper left')\n",
    "    ax2.legend(loc='upper right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded pretrained model gpt2-medium into HookedTransformer\n"
     ]
    }
   ],
   "source": [
    "device = \"cuda:4\"\n",
    "# NBVAL_IGNORE_OUTPUT\n",
    "gpt2_medium = HookedTransformer.from_pretrained(\n",
    "    model_name= \"gpt2-medium\",\n",
    "    center_unembed=True,\n",
    "    center_writing_weights=True,\n",
    "    fold_ln=True,\n",
    "    device=device,\n",
    "    refactor_factored_attn_matrices=True,\n",
    ")\n",
    "\n",
    "gpt2_medium = HookedTransformer.from_pretrained(\n",
    "    model_name= \"tinyllama\",\n",
    "    center_unembed=True,\n",
    "    center_writing_weights=True,\n",
    "    fold_ln=True,\n",
    "    device=device,\n",
    "    refactor_factored_attn_matrices=True,\n",
    ")\n",
    "# Get the default device used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tinyllama_path = 'path_to_checkpoint_of_tinyllama'\n",
    "hf_model = transformers.AutoModelForCausalLM.from_pretrained(tinyllama_path)\n",
    "tokenizer = transformers.AutoTokenizer.from_pretrained(tinyllama_path)\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "tokenizer.padding_side = \"left\"\n",
    "\n",
    "model = HookedTransformer.from_pretrained(\n",
    "    model_name= \"tinyllama\",\n",
    "    hf_model=hf_model, \n",
    "    tokenizer=tokenizer,\n",
    "    local_path=tinyllama_path,\n",
    "    center_unembed=False,\n",
    "    center_writing_weights=False,\n",
    "    fold_ln=True,\n",
    "    device=device,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tried to stack head results when they weren't cached. Computing head results now\n"
     ]
    }
   ],
   "source": [
    "China = ComponentAnalyzer(gpt2_medium,'The official language of France is',' French','France')\n",
    "# China.get_min_rank_at_subject(gpt2_medium.W_U, China.answer_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_rank_logits(gpt2_medium,China)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "coloraxis": "coloraxis",
         "hovertemplate": "Keys: %{x}<br>Queries: %{y}<br>color: %{z}<extra></extra>",
         "name": "0",
         "type": "heatmap",
         "xaxis": "x",
         "yaxis": "y",
         "z": [
          [
           0.03382047638297081,
           0,
           0,
           0,
           0,
           0
          ],
          [
           0.01814335584640503,
           0.0030077374540269375,
           0,
           0,
           0,
           0
          ],
          [
           0.006582174450159073,
           0.0003319525858387351,
           0.0024113140534609556,
           0,
           0,
           0
          ],
          [
           0.019162693992257118,
           0.0014102279674261808,
           0.0069693769328296185,
           0.009665858000516891,
           0,
           0
          ],
          [
           0.0040832567028701305,
           0.0006767817540094256,
           0.0018718497594818473,
           0.003016640432178974,
           0.03257379308342934,
           0
          ],
          [
           0.008011515252292156,
           0.0005897400551475585,
           0.02646312676370144,
           0.006413022056221962,
           0.5983673930168152,
           0.05209408327937126
          ]
         ]
        }
       ],
       "layout": {
        "coloraxis": {
         "cmid": 0,
         "colorscale": [
          [
           0,
           "rgb(103,0,31)"
          ],
          [
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   "source": [
    "draw_attention_pattern(China,gpt2_medium,20,6)"
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  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 450x1800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_output_pattern_with_text(China.heads_out['L20H6'],gpt2_medium,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "circuit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
